Evidence map›Paper›PMID 37650027›Full record

ArticleInternational journal of population data science2022

A methodology to facilitate critical care research using multiple linked electronic, clinical and administrative health records at population scale.

Rowena Griffiths, Laura Herbert, Ashley Akbari, Rowena Bailey, Joe Hollinghurst, Richard Pugh, Tamas Szakmany, Fatemeh Torabi, Ronan A Lyons

Open access · goldAbstract read
In one paragraph

Article in International journal of population data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Rowena GriffithsPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Laura HerbertPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Ashley AkbariPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Rowena BaileyPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Joe HollinghurstPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Richard PughDepartment of Anaesthetics, Glan Clwyd Hospital, Betsi Cadwaladr University Health Board, Rhyl, UK.
Tamas SzakmanyDepartment of Anaesthesia, Intensive Care and Pain Medicine, Division of Population Medicine, Cardiff University, Cardiff, UK.
Fatemeh TorabiPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Ronan A LyonsPopulation Data Science, Health Data Research UK, Swansea University Medical School, Swansea University, Wales, UK.
Swansea University · GBGlan Clwyd Hospital · GBRoyal Gwent Hospital · GB

Funding

British Heart FoundationChief Scientist OfficeMedical Research Council MR/V028367/1Wellcome Trust
6 · The paper itself

Abstract

Introduction: Critical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research. Objective: To describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care. Method: To demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales. Results: When applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission. Conclusion: This methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

Indexed as

Critical CareIntensive Care UnitsElectronicsHealth FacilitiesHospitalizationHumanscritical careelectronic health recordsICNARCintensive carelinkable research data

Identifiers

PMID37650027
PMCPMC10464871
OpenAlexW4285802405

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.